Performance Prediction of Mechanical Draft Wet Cooling Tower Using Artificial Neural Network

• DOI: 10.4028/www.scientific.net/amr.1070-1072.1994
• Publication Date: 2014-12-11T15:15:37Z
• Authors: Zhe Tian Xu; Jia Chen Mao; Yi Qun Pan; Zhi Zhong Huang
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

This paper proposed a prediction approach for the performance of the mechanical draft wet cooling tower based on artificial neural network (ANN). The inlet water temperature, the ambient wet bulb temperature and the ratio of water to air mass flow rate in the cooling tower were selected as the input parameters of a four-layer back propagation neural network (BPNN) to predict the temperature of the water at the tower outlet. After the test of the available data set, the BPNN results in a correlation coefficient of 0.9 between the predicted and experimental values. Thus the prediction performance is good and such prediction approach proves to be feasible and effective.